Background: Household air pollution (HAP) from cooking with solid fuels has been associated with adverse respiratory effects, but most studies use surveys of fuel use to define HAP exposure, rather than on actual air pollution exposure measurements.
Objective: To examine associations between household and personal fine particulate matter (PM) and black carbon (BC) measures and respiratory symptoms.
Methods: As part of the Prospective Urban and Rural Epidemiology Air Pollution study, we analyzed 48-h household and personal PM and BC measurements for 870 individuals using different cooking fuels from 62 communities in 8 countries (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Self-reported respiratory symptoms were collected after monitoring. Associations between PM and BC exposures and respiratory symptoms were examined using logistic regression models, controlling for individual, household, and community covariates.
Results: The median (interquartile range) of household and personal PM was 73.5 (119.1) and 65.3 (91.5) μg/m, and for household and personal BC was 3.4 (8.3) and 2.5 (4.9) x10 m, respectively. We observed associations between household PM and wheeze (OR: 1.25; 95%CI: 1.07, 1.46), cough (OR: 1.22; 95%CI: 1.06, 1.39), and sputum (OR: 1.26; 95%CI: 1.10, 1.44), as well as exposure to household BC and wheeze (OR: 1.20; 95%CI: 1.03, 1.39) and sputum (OR: 1.20; 95%CI: 1.05, 1.36), per IQR increase. We observed associations between personal PM and wheeze (OR: 1.23; 95%CI: 1.00, 1.50) and sputum (OR: 1.19; 95%CI: 1.00, 1.41). For household PM and BC, associations were generally stronger for females compared to males. Models using an indicator variable of solid versus clean fuels resulted in larger OR estimates with less precision.
Conclusions: We used measurements of household and personal air pollution for individuals using different cooking fuels and documented strong associations with respiratory symptoms.
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http://dx.doi.org/10.1016/j.envres.2022.113430 | DOI Listing |
JMIR Pediatr Parent
December 2024
CAMHS Digital Lab,Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
Background: Young people and families endure protracted waits for specialist mental health support in the United Kingdom. Staff shortages and limited resources have led many organizations to develop digital platforms to improve access to support. myHealthE is a digital platform used by families referred to Child and Adolescent Mental Health Services in South London.
View Article and Find Full Text PDFJMIR Form Res
December 2024
Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States.
Background: Remote blood pressure (BP) monitoring (RBPM) or BP telemonitoring is beneficial in hypertension management. People with hypertension involved in telemonitoring of BP often have better BP control than those in usual care. However, most reports on RBPM are from intervention studies.
View Article and Find Full Text PDFFront Public Health
December 2024
School of Public Health, Southern Medical University, Guangzhou, China.
Introduction: Falls are the primary cause of unintentional fatalities among individuals aged 65 and older. Enhancing research on fall prevention among older adults is an urgent priority. Consequently, this study aims to investigate the prevalence and influencing factors of falls among community-dwelling older adults in Guangzhou, China, with a particular emphasis on the impact of family functioning.
View Article and Find Full Text PDFFront Public Health
December 2024
Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States.
Background: The 2020 Nagorno-Karabakh conflict resulted in displacement of approximately 90,000 ethnic Armenians from Nagorno-Karabakh to Armenia, exacerbating existing vulnerabilities in the region. This study investigated food insecurity among displaced populations and host communities in Armenia during the conflict.
Methods: This study is a secondary analysis of cross-sectional data obtained from the 2020 REACH ARM Database Multi-Sector Needs Assessment (MSNA), which was conducted across six Armenian provinces.
Front Public Health
December 2024
Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Introduction: Dynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002-2016 was used to analyze the complex relationships among various covariates influencing child undernutrition.
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